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Volumetric reconstruction of fetal brains from multiple stacks of MR slices is challenging due to severe subject motion and image artifacts. We propose a deep learning method to solve the slice-to-volume reconstruction problem in two stages. First, a Transformer network is used to correct motion between slices by registering the input slices to a 3D canonical space. Second, an implicit neural network reconstructs the 3D volume by learning a continuous 3D representation of the fetal brain from the 2D observations. Results show that our method achieves high reconstruction quality and outperforms existing state-of-the-art methods in presence of severe fetal motion.
Xu et al. (Wed,) studied this question.